COMPARE/Markin and your stack
Markin + Hightouch: warehouse data, decisions and activation
+17–35% ARPU against holdoutObserved range across Markin deployments, measured on treated cohorts.
Hightouch is a composable CDP and activation layer: it models audiences on top of your warehouse and syncs them to downstream destinations without copying the data. Markin uses that same warehouse to decide which commercial opportunity is worth acting on per customer, and lets the activation route stay exactly where it is.
What is at stake
A decision layer is not a tool line item. It moves ARPU on the whole base, every month.
Installed base
1.5M
customers at $28 ARPU / month
Addressable revenue
$241.9M
per year, reachable base
Verified ARPU uplift
+17% to +35% ARPU
on treated cohorts, against holdout
What that is worth
$41.1M – $84.7M
incremental revenue per year
Measured on treated cohorts against a randomised holdout, read over a full measurement window rather than the first weeks. Anonymised range across Markin deployments in large B2C bases; your own holdout is the number that decides. The figures above apply that range to the reachable share of the base on this page's assumptions; they are arithmetic, not a forecast for your business.
Run it on your own numbersWhat your stack does today.
01
Hightouch
A composable CDP built on the warehouse: audience modelling, identity resolution and reverse-ETL syncs that push customer data from the warehouse into marketing, sales and support destinations.
02
Markin, the decision + execution layer
A layer that reads the same warehouse and produces ranked, sized commercial decisions per customer, with a treatment choice, a control group and a measured incremental result.
Side by side
The differences that change outcomes.
| Dimension | Hightouch | Markin, the decision + execution layer |
|---|---|---|
| Question it answers | How do we get this warehouse data into the tools that need it? | Which opportunity is worth acting on for this customer, and what is it worth? |
| Primary input | Warehouse tables and models, audience definitions, destination mappings. | Warehouse context, outcome history, margins, contact costs and constraints. |
| Primary output | Synced audiences, profiles and traits in downstream destinations. | A decision per customer, materialised back into the warehouse for activation. |
| Usual owner | Data engineering and marketing operations. | Growth and data science. |
| How it's measured | Sync reliability and latency, audience coverage, destination match rates. | Incremental revenue and ARPU against a holdout. |
The unsolved part
What a composable stack still leaves open
Once the warehouse is the source of truth and syncs are reliable, the bottleneck moves upstream of activation: someone still has to decide which of the many possible audiences deserves the customer's attention this week, and whether it moved anything.
- An audience is a rule over the warehouse. It does not estimate the incremental effect of acting on it.
- Multiple models can qualify the same customer at once, and priority is resolved by ordering rather than expected value.
- Nothing enforces holdouts by default, so incremental effect has to be reconstructed after the fact.
- Hypothesis generation stays manual: the number of ideas tested is bounded by analyst time.
The actual difference
Markin is not another decisioning engine.
Markin is not a decisioning engine. A decisioning engine ranks actions a human already defined. Markin works like a data science and growth team: it forms its own hypotheses about why ARPU is stuck, marketing, product, pricing or technical, sizes them, executes them inside the systems you already run, and reads each one against a holdout.
| A decisioning engine | Markin | |
|---|---|---|
| Where the hypothesis comes from | A human authors it. The engine chooses between options someone already approved. | Markin authors it. It reads the base, finds where revenue is leaking or unclaimed, and writes the hypothesis itself. |
| What it is allowed to question | Message, offer, channel, timing, inside the campaign surface it was given. | Anything that moves ARPU: onboarding friction, pricing and packaging, a feature nobody adopts, a payment failure spike, a broken deeplink. |
| Who does the analysis | Your analysts, before and after. The engine optimises; it does not investigate. | Markin does the analysis. Sizing, segment definition, experiment design and readout are automated end to end. |
| Where it stops | At the recommendation. Someone still has to build and launch it. | It launches. Markin executes inside your existing platforms and product surfaces, then closes the loop on the result. |
| Throughput | As many hypotheses as your roadmap has room for, typically a handful per quarter. | Hundreds in parallel, every one carrying a control group. |
| What happens when it is wrong | The programme keeps running until someone reviews it. | It is retired automatically. Failing to beat control is a normal, cheap outcome. |
A decisioning engine picks the best action from a list you wrote. Markin writes the list, and runs it in your stack.
Hypothesis space
Everything a human growth scientist would look at.
Most growth problems are not message problems. Markin is not restricted to the campaign surface: if something is holding ARPU back, it is in scope, and it gets tested the same way.
Marketing
The classic surface, but chosen per customer rather than per segment, and always against a holdout.
- Which offer this specific customer is worth making
- Channel and timing chosen per person, not per campaign
- Contact pressure and fatigue arbitrated across every programme
- Win-back economics: who is worth a discount and who is not
Product
Where the customer actually experiences the value, and where most silent revenue loss happens.
- Onboarding steps that lose customers before first value
- A feature with high retention correlation that half the base never discovers
- Paywall and upgrade prompt placement
- In-product surfaces used as a treatment arm, not just email and push
Commercial
Pricing, packaging and the shape of the offer itself, tested rather than argued about.
- Plan and bundle structure by cohort
- Discount depth against margin, not against conversion alone
- Annual versus monthly framing per customer
- Dunning and involuntary churn recovery sequences
Technical health
Anomalies nobody asked it to look for. This is the category no decisioning engine covers.
- A checkout error rate that rose on one device and one region
- Payment failures concentrated in a single issuer or method
- A broken deeplink quietly killing a high-value journey
- Latency or delivery degradation eating conversion before any message does
Think of Markin as a data science and growth team that never sleeps: it investigates, forms hypotheses, ships them into your own stack and proves each one against a control group, at a volume no human team can reach.
Their decisioning layer
What Hightouch decides — and where it stops.
Hightouch AI Decisioning is the closest thing in this list to a true decision layer: reinforcement learning agents choose message, channel and timing per user. Its documented boundaries are what separate it from revenue decisioning, it selects between messages you pre-approve, and it needs someone else's platform to send them.
Products referenced: AI Decisioning (AID), Customer Data & AI Platform
What it optimises
AI Decisioning uses reinforcement learning and AI agents to automatically choose the best content, offer, channel and timing.
Vendor pageHightouch, What is AI Decisioning (Feb 2026)An agent combines an audience, goals and messages; AID then learns which message, channel and timing perform best for each person, balancing exploration and exploitation.
Vendor docsHightouch docs, AI Decisioning overview
Documented boundaries
Hightouch documents that agents operate entirely within the inputs and rules you define: they do not create new content or act outside your configuration.
Vendor docsHightouch docs, AgentsAID does not send. It requires at least one connected delivery destination, Salesforce Marketing Cloud, Braze or Iterable, so its reach is bounded by whichever channels that platform supports.
Vendor docsHightouch docs, AI Decisioning overviewAI Decisioning requires workspace configuration from data teams before marketers can create agents.
Vendor docsHightouch docs, AI Decisioning overview
What the evidence actually says
Hightouch publishes lift metrics as a product capability inside its Insights dashboard, but no aggregate uplift percentage for AID appears on its public pages.
Vendor pageHightouch, AI Decisioning platform page
Where Markin is different.
Opportunity first, message second
AID optimises between messages that already exist. Markin starts a step earlier: it generates and sizes the revenue opportunity, then decides whether any treatment clears the bar. Message selection is the last step, not the whole job.
Economics in the objective function
Markin ranks on expected incremental revenue net of margin, discount cost and contact cost. Reward defined as engagement will happily optimise towards cheap conversions that would have happened anyway.
No delivery-platform dependency
Markin's decision is written wherever it is needed, an engagement platform, the warehouse, a service desk, a product surface, rather than requiring Braze, Iterable or SFMC to exist first.
Architecture
How the two run together
Warehouse as the shared substrate
Markin reads the same modelled tables Hightouch activates from. No data is copied into a separate profile store and no second identity graph is created.
Decision written back to the warehouse
Markin materialises ranked, sized decisions and control-group assignment as tables in the warehouse, so they are queryable, auditable and versioned alongside everything else.
Activation stays in Hightouch
Those decision tables become the source for syncs to email, SMS, ads, product surfaces or support tools. The activation architecture does not change.
The last step is execution, not a hand-off. Markin does not email a recommendation to someone who then has to build it: it launches the treatment inside Hightouch and your product surfaces directly, with the holdout attached, and reads the result itself.
The loop
Execution is a step in the loop, not a hand-off.
- 01
Observe
Markin reads the behavioural, transactional and product signal you already collect, continuously.
- 02
Hypothesise
It writes the hypothesis itself, marketing, product, commercial or technical, and states the expected direction.
- 03
Size
Each opportunity is ranked by expected value, so the queue is ordered by money rather than by opinion.
- 04
Design
Segment, treatment, guardrails and a randomised holdout are set before anything ships.
- 05
Execute
It launches inside the systems you already run, your engagement platform, your product surfaces, your APIs. Nothing waits on a build queue.
- 06
Read
Results are measured against the holdout over a full window, so novelty is not mistaken for effect.
- 07
Scale or retire
What beats control is scaled across the base. What does not is switched off automatically.
Where Markin fits
Not a replacement. A growth-science team on top.
A composable stack already made the right architectural choice: the warehouse is the source of truth. Markin adds the layer that turns that truth into ranked commercial decisions, and writes them back to the warehouse so activation continues through the syncs you already trust.
No second source of truth
Decisions are warehouse tables. Anyone can inspect why a customer received a treatment, and finance can reconcile the numbers against the same data.
Experimentation is built in
Control-group assignment is part of the decision, not an afterthought bolted on before a QBR.
Hypotheses at data speed
The layer generates and sizes candidate opportunities continuously, so the backlog is not limited to what an analyst had time to write this sprint.
Operating model
The constraint is not ideas. It is how many you can test.
| Today, with Hightouch | With Markin on top | |
|---|---|---|
| Revenue hypotheses tested per quarter | 4 to 8, whatever the roadmap had room for | Hundreds, generated and run in parallel |
| What can be hypothesised about | Messages, offers and audiences, the campaign surface | Marketing, product, pricing and technical health alike |
| From decision to live in the channel | A ticket, a build queue, a release window | Markin launches it in your existing platforms itself |
| Time from idea to a result you trust | 6 to 10 weeks of analysis, build and readout | Days, because sizing and design are automated |
| Share of decisions with a control group | The flagship programmes, when there is time | Every decision, by default |
| Coverage of the base | Top segments and the customers a rule caught | One decision per customer, across the whole base |
| Cost of testing the 500th hypothesis | Another analyst, another quarter | Effectively zero |
| What the team spends its time on | Pulling data, building lists, reconciling reports | Judgement: constraints, economics, what to scale |
Markin does not replace your data science team. It removes the ceiling on how much of the base that team can act on, and how fast it finds out whether it worked.
What Markin does not replace.
To be explicit about scope, because procurement will ask:
- Markin is not a reverse-ETL tool and does not sync data to destinations.
- Markin does not replace warehouse modelling, identity resolution or audience management.
- Markin does not require data to leave the warehouse architecture you chose.
- Markin does not own channel delivery in any downstream tool.
- Markin does not sit beside Hightouch making suggestions. It drives it, the action is launched there, in the system your team already knows, and the result comes back into the loop.
Evidence standard
Most of this category reports its own lift.
None of the major engagement, CDP or personalisation vendors publishes an independently verified uplift figure for its decisioning product. Where numbers exist, they come from vendor-commissioned studies or single-customer case studies with no disclosed holdout methodology. The most rigorous public research in the category is not flattering to anyone, including us, which is exactly why we build against it.
BCG reports that when organisations adopt rigorous incrementality testing, they typically find 20% to 40% of their active next-best-action programmes deliver marginal to negative lift.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)The same research flags novelty effects, new programmes show inflated early results, and recommends 8 to 12 weeks before drawing conclusions.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)Global-holdout, programme-level ROI measurements often overstate impact through halo effects, pull-forward effects and experiment contamination.
Independent researchBCG, How Measurement Is Evolving in Next-Best Action (2026)
How Markin holds itself to it
- Every decision Markin makes carries a control group. Uplift is reported against that holdout, not against the customers who did not qualify.
- Results are read over a full measurement window rather than in the first weeks, so novelty is not mistaken for effect.
- Programmes that fail to beat control are retired automatically. Killing decisions that do not pay is part of the loop, not an annual review.
- The one figure we quote about ourselves is a range, not an average: +17% to +35% ARPU on treated cohorts against a randomised holdout, across Markin deployments in large B2C bases. We publish no industry benchmark, because we could not source one we would be willing to defend. Your holdout is the number that matters.
Size it yourself
Size the decision layer against a warehouse-native setup
Preloaded for a data-mature team running Hightouch on top of the warehouse: good reach, lean programme cost, and a send platform already in place. Reinforcement learning optimises within the actions and rules you define, the figure below is what it is worth to generate and size those opportunities in the first place.
Your base
Accounts that generated revenue in the last 30 days. Not registered users.
Recurring plus non-recurring revenue divided by active customers.
Margin on the next unit sold, not blended company margin.
Your programme today
Consented, non-fatigued, reachable on at least one channel.
Revenue lost to cancellations each month, as a share of the base.
The bet
Licences, data, incentives and the people running it.
Before any incrementality haircut. 2–4% is a defensible planning assumption.
Verified annual impact
$3.3M
Net incremental gross margin in the central case, after the programme cost and after the share of decisioning programmes that independent research finds deliver no real lift.
Reported uplift
$8.5M
What a before/after dashboard would claim, with no control group.
Verified uplift
$5.9M
What survives a holdout in the central case.
Return on programme cost
5.4×
Payback
3 mo
If 20–40% of it does nothing
What it takes to prove it
To detect a 3.5% lift on revenue per customer you need roughly 29K customers in the control arm, about 4.1% of your addressable base, read over at least 8 weeks, so novelty is not mistaken for effect.
Addressable base
720K
Revenue at risk from churn
$118.1M
Annualised, at the current monthly rate.
Time to value
90 days to a number that survived a holdout.
No replatform, no data migration, no rebuild of the channels you already run. If the first cohorts do not beat control, nothing scales and you have lost a quarter, not a roadmap.
Weeks 0–2
Read the context you already have
Markin connects to the data and the channels you run today, Hightouch included. No migration, no replatform, no new source of truth.
Weeks 3–6
First sized opportunities in test
Opportunities are ranked by expected value, treatments are chosen per customer, and the first cohorts go live with a randomised holdout attached.
Weeks 7–12
First verified incremental revenue
Results are read over a full measurement window. What beats control scales; what does not is retired. Nothing scales on a number that has not survived a holdout.
When you don’t need Markin.
- Your warehouse does not yet hold reliable outcome data, so no decision can be evaluated.
- Activation is the bottleneck rather than prioritisation, solve the syncs first.
- You are looking for reverse-ETL. Markin does not move data into destinations; that is what your activation layer is for.
Questions buyers ask.
How is Markin different from Hightouch AI Decisioning?
Hightouch AI Decisioning uses reinforcement learning to pick the best message, channel and timing per user. Its own documentation notes that agents operate entirely within the inputs and rules you define, that delivery requires a connected Braze, Iterable or Salesforce Marketing Cloud destination, and that data teams must configure the workspace first. Markin starts a step earlier: it generates and sizes the revenue opportunity, ranks options on expected incremental margin, and is not dependent on a third-party send platform.
Do we have to replace Hightouch?
No. Activation stays where it is. Markin writes its decisions back into the warehouse and those tables become the source for your existing syncs.
Hightouch also offers AI Decisioning. How is this different?
They overlap in ambition and are worth evaluating side by side. AI Decisioning optimises the content, offer, channel and timing of individual campaigns. Markin's emphasis is upstream of that: generating and sizing revenue opportunities across the customer base and ranking which ones deserve to be acted on at all. A direct comparison page is on the way; in the meantime the honest answer is that the right fit depends on whether your bottleneck is message optimisation or opportunity prioritisation.
Does Markin copy our data?
No. It reads the modelled tables in place and writes decisions back as tables. The warehouse stays the source of truth.
Who maintains the models?
Your data team keeps owning the warehouse models. Markin consumes them and adds the decision and experiment layer on top, with the logic visible rather than hidden in a black box.
How is Markin different from the decisioning or AI already inside Hightouch?
A decisioning engine ranks actions a human already defined, inside the campaign surface it was given. Markin forms the hypotheses itself, marketing, product, pricing or a technical anomaly holding growth back, sizes them, executes them inside Hightouch and your product surfaces, and reads each one against a randomised holdout. It behaves like a data science and growth team, not like an optimiser.
Does Markin only test messages and offers?
No. Anything a human growth scientist would investigate is in scope: onboarding friction, feature adoption, pricing and packaging, dunning, and technical health issues such as a checkout error rate or a broken deeplink quietly killing conversion. Marketing is one of four hypothesis domains, not the boundary.
What is the business case for adding Markin on top of Hightouch?
On the assumptions preloaded above, 1.5M customers at 28 a month, a small move in ARPU is a large number in absolute terms, because it applies to the whole installed base every month rather than to a campaign. Across Markin deployments the verified range on treated cohorts is +17% to +35% ARPU against a randomised holdout. The point is not more messages: it is finding the highest-value action per customer, launching it, and proving it against control before it scales.
How long before it pays for itself?
First sized opportunities are in test within six weeks and the first holdout-verified result lands inside 90 days. Payback depends on your base, margin and programme cost, the calculator on this page computes it from your own numbers, after applying the 20% to 40% haircut BCG finds when next-best-action programmes are incrementality-tested.